2 citations · 2 across the 19 of their papers we have counts for
21 papers
Narrowing the Collaboration Gap, Probably
Mirah Shi, Marcel Hussing, Ira Globus-Harris +3
Large language models are increasingly deployed as teams of agents that hold different private information about a shared task. How should such agents concisely communicate in a wa…
Delegating Authorization to Misaligned Agents: Coalitional Alignment and Safe Control
Natalie Collina, Surbhi Goel, Aaron Roth +1
Long-running AI agents create a control problem: each action they take changes the state, which in turn affects the trajectory of future actions. If the agent is not fully aligned,…
The Sample Complexity of Multicalibration
Natalie Collina, Jiuyao Lu, Georgy Noarov +1
We study the minimax sample complexity of multicalibration in the batch setting. A learner observes i.i.d. samples from an unknown distribution and must output a (possibly rand…
Personalization Aids Pluralistic Alignment Under Competition
Natalie Collina, Surbhi Goel, Aaron Roth +1
Can competition among misaligned AI providers yield aligned outcomes for a diverse population of users, and what role does model personalization play? We study a setting where mult…
Recommending Best Paper Awards for ML/AI Conferences via the Isotonic Mechanism
Garrett G. Wen, Buxin Su, Natalie Collina +2
Machine learning and artificial intelligence conferences such as NeurIPS and ICML now regularly receive tens of thousands of submissions, posing significant challenges to maintaini…
Optimal Lower Bounds for Online Multicalibration
Natalie Collina, Jiuyao Lu, Georgy Noarov +1
We prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration. In the general setting where group functions ca…
Breaking Algorithmic Collusion in Human-AI Ecosystems
Natalie Collina, Eshwar Ram Arunachaleswaran, Meena Jagadeesan
AI agents are increasingly deployed in ecosystems where they repeatedly interact not only with each other but also with humans. In this work, we study these human-AI ecosystems fro…
How to Find Fantastic AI Papers: Self-Rankings as a Powerful Predictor of Scientific Impact Beyond Peer Review
Buxin Su, Natalie Collina, Garrett Wen +5
Peer review in academic research aims not only to ensure factual correctness but also to identify work of high scientific potential that can shape future research directions. This…
Emergent Alignment via Competition
Natalie Collina, Surbhi Goel, Aaron Roth +2
Aligning AI systems with human values remains a fundamental challenge, but does our inability to create perfectly aligned models preclude obtaining the benefits of alignment? We st…
Collaborative Prediction: Tractable Information Aggregation via Agreement
Natalie Collina, Ira Globus-Harris, Surbhi Goel +3
We give efficient "collaboration protocols" through which two parties, who observe different features about the same instances, can interact to arrive at predictions that are more…
Swap Regret and Correlated Equilibria Beyond Normal-Form Games
Eshwar Ram Arunachaleswaran, Natalie Collina, Yishay Mansour +3
Swap regret is a notion that has proven itself to be central to the study of general-sum normal-form games, with swap-regret minimization leading to convergence to the set of corre…
Learning to Play Against Unknown Opponents
Eshwar Ram Arunachaleswaran, Natalie Collina, Jon Schneider
We consider the problem of a learning agent who has to repeatedly play a general sum game against a strategic opponent who acts to maximize their own payoff by optimally responding…
Tractable Agreement Protocols
Natalie Collina, Surbhi Goel, Varun Gupta +1
We present an efficient reduction that converts any machine learning algorithm into an interactive protocol, enabling collaboration with another party (e.g., a human) to achieve co…
The Value of Ambiguous Commitments in Multi-Follower Games
Natalie Collina, Rabanus Derr, Aaron Roth
We study games in which a leader makes a single commitment, and then multiple followers (each with a different utility function) respond. In particular, we study ambiguous commitme…
Algorithmic Collusion Without Threats
Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan +2
There has been substantial recent concern that pricing algorithms might learn to ``collude.'' Supra-competitive prices can emerge as a Nash equilibrium of repeated pricing games, i…
The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review
Buxin Su, Jiayao Zhang, Natalie Collina +6
We conducted an experiment during the review process of the 2023 International Conference on Machine Learning (ICML), asking authors with multiple submissions to rank their papers…
Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability
Natalie Collina, Varun Gupta, Aaron Roth
We study a repeated contracting setting in which a Principal adaptively chooses amongst Agents at each of rounds. The Agents are non-myopic, and so a mechanism for the Prin…
Pareto-Optimal Algorithms for Learning in Games
Eshwar Ram Arunachaleswaran, Natalie Collina, Jon Schneider
We study the problem of characterizing optimal learning algorithms for playing repeated games against an adversary with unknown payoffs. In this problem, the first player (called t…
An Elementary Predictor Obtaining Distance to Calibration
Eshwar Ram Arunachaleswaran, Natalie Collina, Aaron Roth +1
Blasiok et al. [2023] proposed distance to calibration as a natural measure of calibration error that unlike expected calibration error (ECE) is continuous. Recently, Qiao and Zhen…
Efficient Prior-Free Mechanisms for No-Regret Agents
Natalie Collina, Aaron Roth, Han Shao
We study a repeated Principal Agent problem between a long lived Principal and Agent pair in a prior free setting. In our setting, the sequence of realized states of nature may be…
Efficient Stackelberg Strategies for Finitely Repeated Games
Natalie Collina, Eshwar Ram Arunachaleswaran, Michael Kearns
We study Stackelberg equilibria in finitely repeated games, where the leader commits to a strategy that picks actions in each round and can be adaptive to the history of play (i.e.…